Graph-based analysis and visualization of experimental results with ONDEX

Köhler, Jacob; Baumbach, Jan; Taubert, Jan; Specht, Michael; Skusa, Andre; Rüegg, Alexander; Rawlings, Christopher J.; Verrier, P. J.; Philippi, Stephan

doi:10.1093/bioinformatics/btl081

Cited by 189 publications

(125 citation statements)

References 17 publications

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“…In addition to standard database approaches, graph and ontology oriented approaches are being used with increasing success. While some of these graph based frameworks can be found in commercial products such as VTT (Gopalacharyulu, et al, 2005), ChipInspector / Bibliosphere Pathway Edition (from Genomatix), Phylosopher (from Genedata), ExPlain (from BIOBASE) and PathwayStudio (Nikitin, et al, 2003) (from Ariadne Genomics), some non-commercial systems are also available free of charge such as PathSys (Baitaluk, et al, 2006), BN++ (Küntzer, et al, 2006) and the ONDEX system (Köhler, et al, 2006;. Several projects have also followed a similar approach using the principles established for the semantic web such as SWEDI (Post, et al, 2007) or BioDASH (Neumann and Quan, 2006).…”

Section: Approaches To Data Integrationmentioning

confidence: 99%

“…Furthermore, concepts and relations may have attributes and optional characteristics attached to them. A formal description of the data structure of the ON-DEX system is presented by (Köhler, et al, 2006). During data import, consistency checks on the data are performed, e.g.…”

Section: Data Importmentioning

confidence: 99%

“…The resulting graphs can be exported in an ONDEX specific XML or RDF dialect for which generic exporters are available (Taubert, et al, 2007) and loaded into the ONDEX Visualisation Tool Kit (OVTK) (Köhler, et al, 2006) for further analysis. In the latest ONDEX release, workflows are not restricted to the example given in this publication and can easily be extended with user-defined functionality through a plug-in architecture.…”

Section: Data Filtering and Knowledge Extractionmentioning

confidence: 99%

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Graph-based sequence annotation using a data integration approach

Pesch

Lysenko

Hindle

et al. 2008

Journal of Integrative Bioinformatics

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SummaryThe automated annotation of data from high throughput sequencing and genomics experiments is a significant challenge for bioinformatics. Most current approaches rely on sequential pipelines of gene finding and gene function prediction methods that annotate a gene with information from different reference data sources. Each function prediction method contributes evidence supporting a functional assignment. Such approaches generally ignore the links between the information in the reference datasets. These links, however, are valuable for assessing the plausibility of a function assignment and can be used to evaluate the confidence in a prediction. We are working towards a novel annotation system that uses the network of information supporting the function assignment to enrich the annotation process for use by expert curators and predicting the function of previously unannotated genes. In this paper we describe our success in the first stages of this development. We present the data integration steps that are needed to create the core database of integrated reference databases (UniProt, PFAM, PDB, GO and the pathway database AraCyc) which has been established in the ONDEX data integration system. We also present a comparison between different methods for integration of GO terms as part of the function assignment pipeline and discuss the consequences of this analysis for improving the accuracy of gene function annotation. The methods and algorithms presented in this publication are an integral part of the ON-DEX system which is freely available from http://ondex.sf.net/.

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Section: Approaches To Data Integrationmentioning

confidence: 99%

Section: Data Importmentioning

confidence: 99%

Section: Data Filtering and Knowledge Extractionmentioning

confidence: 99%

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Graph-based sequence annotation using a data integration approach

Pesch

Lysenko

Hindle

et al. 2008

Journal of Integrative Bioinformatics

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“…This is particularly true for several tools which have been developed in the domain of Life Sciences for the analysis of biological networks [6], some of which (e.g. :Ondex [13], Cytoscape [15]) are inherently domain independent. It should be noted that the Life Sciences present a number of information related issues that makes the Semantic Web an ideal solution, and computational biologists and bioinformaticians have been among the most enthusiastic adopters of these technologies [14].…”

Section: Introductionmentioning

confidence: 99%

Lost in Translation: Data Integration Tools Meet the Semantic Web (Experiences from the Ondex Project)

Splendiani

Rawlings

Kuo

et al. 2012

Lecture Notes in Electrical Engineering

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Abstract-More information is now being published in machine processable form on the web and, as de-facto distributed knowledge bases are materializing, partly encouraged by the vision of the Semantic Web, the focus is shifting from the publication of this information to its consumption. Platforms for data integration, visualization and analysis that are based on a graph representation of information appear first candidates to be consumers of web-based information that is readily expressible as graphs. The question is whether the adoption of these platforms to information available on the Semantic Web requires some adaptation of their data structures and semantics. Ondex is a network-based data integration, analysis and visualization platform which has been developed in a Life Sciences context. A number of features, including semantic annotation via ontologies and an attention to provenance and evidence, make this an ideal candidate to consume Semantic Web information, as well as a prototype for the application of network analysis tools in this context. By analyzing the Ondex data structure and its usage, we have found a set of discrepancies and errors arising from the semantic mismatch between a procedural approach to network analysis and the implications of a web-based representation of information. We report in the paper on the simple methodology that we have adopted to conduct such analysis, and on issues that we have found which may be relevant for a range of similar platforms.

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“…The centre uses mathematical and computational modelling techniques developed at Imperial College London [2]. The background knowledge in these applications is generated by a system called Ondex [7]. Ondex is unique in its ability to generate large amounts of Prolog background knowledge on cell biochemistry by parsing, filtering and combining various publicly-available databases.…”

Section: Introductionmentioning

confidence: 99%

Variation of Background Knowledge in an Industrial Application of ILP

Muggleton

Chen

Watanabe

et al. 2011

Inductive Logic Programming

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Abstract. In several recent papers ILP has been applied to Systems Biology problems, in which it has been used to fill gaps in the descriptions of biological networks. In the present paper we describe two new applications of this type in the area of plant biology. These applications are of particular interest to the agrochemical industry in which improvements in plant strains can have benefits for modelling crop development. The background knowledge in these applications is extensive and is derived from public databases in a Prolog format using a new system called Ondex (developers BBSRC Rothamsted). In this paper we explore the question of how much of this background knowledge it is beneficial to include, taking into account accuracy increases versus increases in learning time. The results indicate that relatively shallow background knowledge is needed to achieve maximum accuracy.

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Graph-based analysis and visualization of experimental results with ONDEX

Cited by 189 publications

References 17 publications

Graph-based sequence annotation using a data integration approach

Graph-based sequence annotation using a data integration approach

Lost in Translation: Data Integration Tools Meet the Semantic Web (Experiences from the Ondex Project)

Variation of Background Knowledge in an Industrial Application of ILP

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